Study on effective self-learning for aerial vehicles using emulated flight test environment based on magnetic levitation자기부상 풍동 기반 지상 모사 비행시험을 통한 무인기의 효율적 자가 학습에 관한 연구

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dc.contributor.advisorHan, Jae-Hung-
dc.contributor.authorSung, Yeol-Hun-
dc.description학위논문(박사) - 한국과학기술원 : 항공우주공학과, 2021.2,[vi, 105 p. :]-
dc.description.abstractit allows the MAV physical model to learn how to fly by itself, without the risk of damage. The proposed methodology utilizes a magnetic levitation based safety-guaranteed flight test environment and deep reinforcement learning techniques to avoid the current problems in flight control system design procedures, such as the reality gap issue of computational simulations, and the safety issues inherent to real flight testing. The safety-guaranteed flight test environment was achieved using a developed magnetic suspension and balance system (MSBS), which dynamically adjusts magnetic forces interacting with a magnetically levitated MAV. As a result, the MAV can perform either a free flight test for reinforcement learning, or can be constrained for safety. This learning environment was developed to permit safe reinforcement learning of MAV, since trial-and-error based learning typically makes the MAV unstable. In this regard, the MAV can learn to fly itself based on trial-and-error, by interacting with the emulated free flight test environment. Notably, the entire learning process can be conducted without numerical models for both the MAV itself and the flight environment, and the safety of the MAV is guaranteed, even when attempting undesirable actions which might cause the model to become unstable. This approach has unique advantages to permit the effective design of a flight control system, by reducing the modeling errors typical of computational simulations, and preventing the risk of damage in real flight tests. This could eventually enhance the benefits of reinforcement learning for developing advanced flight control systems, which has shown the great potential based on its outstanding control performance with adaptability to mutable dynamics and environments.-
dc.description.abstractThis paper presents a novel methodology for designing the flight control system of a micro aerial vehicle (MAV)-
dc.subjectMagnetic suspension and balance system (MSBS)▼aUnmanned aerial vehicle (UAV)▼aMicro aerial vehicle (MAV)▼aFlight control system▼aArtificial intelligence (AI)▼aDeep reinforcement learning (DRL)▼aWind tunnel test▼aFlight test-
dc.subject자기부상장치▼a무인 비행체▼a초소형 비행체▼a비행 제어 시스템▼a인공지능▼a심층 강화학습▼a풍동시험▼a비행시험-
dc.titleStudy on effective self-learning for aerial vehicles using emulated flight test environment based on magnetic levitation-
dc.title.alternative자기부상 풍동 기반 지상 모사 비행시험을 통한 무인기의 효율적 자가 학습에 관한 연구-
dc.description.department한국과학기술원 :항공우주공학과,-
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